code: def test_use_first_four_layers_of_pretrained(self):
      pretrained = "bert-base-cased"
  
      class SmallTransformer(TokenEmbedder):
          def __init__(self):
              super().__init__()
              self.embeddings = (
                  TransformerEmbeddings.from_pretrained_module(
                      pretrained, relevant_module="bert.embeddings"
                  )
              )
              self.transformer = (
                  TransformerStack.from_pretrained_module(
                      pretrained,
                      num_hidden_layers=4,
                      relevant_module="bert.encoder",
                      strict=False,
                  )
              )
  
          def forward(self, token_ids: torch.LongTensor):
              x = self.embeddings(token_ids)
              x = self.transformer(x)
              return x
  
      small = SmallTransformer()
      assert len(small.transformer.layers) == 4
      small(torch.LongTensor([[0, 1, 2]]))
  
file_path: allennlp/tests/modules/transformer/toolkit_test.py
imports: import pytest
  import torch
  from torch.testing import assert_allclose
  from transformers import AutoModel
  from transformers.models.albert.modeling_albert import AlbertEmbeddings
  from allennlp.common import cached_transformers
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.modules.token_embedders import Embedding, TokenEmbedder
  from allennlp.modules.transformer import TransformerStack, TransformerEmbeddings, TransformerPooler
  from allennlp.common.testing import AllenNlpTestCase
label_desc: Use image node_id 3 for calling the TestTransformerToolkit obj's underlying member method code with example usage: obj.test_use_first_four_layers_of_pretrained() and returns: x